Enaminones 8. CoMFA and CoMSIA studies on some anticonvulsant enaminones

Abstract

3D QSAR studies comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 26 structurally diverse subcutaneous pentylenetetrazol (scPTZ) active enaminone analogues, previously synthesized in our laboratory. CoMFA and CoMSIA were employed to generate models to define the specific structural and electrostatic features essential for enhanced binding to the putative GABA receptor. The 3D QSAR models demonstrated a reliable ability to predict the CLog P of the active anticonvulsant enaminones, resulting in a q² of 0.558 for CoMFA, and a q² of 0.698 for CoMSIA. The outcomes of the contour maps for both models provide detailed insight for the structural design of novel enaminone derivatives as potential anticonvulsant agents.

Introduction

Epilepsy is one of the most frequent neurological afflictions, characterized by excessive temporary neuronal discharges resulting in uncontrolled convulsions.¹ Over the years, the field of epilepsy has received a great deal of attention from research investigators in hopes of discovering new drug agents that will elicit better selectivity and potency, and most importantly have minimal toxic effects. Though several new anticonvulsants are introduced, some types of seizures are still not adequately controlled with the current therapy.^1,2 Toxicity, intolerance, and lack of efficacy for certain types of seizures are some of the limitations of existing antiepileptic medications.^1,3 In view of the broad etiology of the syndrome, epilepsy involves more than one mechanism that may be responsible for the various seizures, thus its response to different therapeutic agents would be expected.⁴ For instance, the voltage-gated sodium channels (VGSCs) are responsible for the initial inward current during the depolarization phase of the action potential in excitable cells⁵, reduction of GABA-ergic transmission⁶, and excessive glutamatergic neurotransmission⁷ are various prominent physiological abnormalities for triggering epileptic seizures.

A number of structurally diverse anticonvulsant active enaminone analogues have been synthesized in our laboratory for over 15 years. During that time, we have discovered several novel, potent entities that have shown good to moderate protection against the maximal electroshock seizure (MES) test and the subcutaneous pentylenetetrazol (scPTZ) test.⁸ The scPTZ seizure model identifies compounds that inhibit the GABA antagonistic effects of pentylenetetrazol or raises the seizure threshold.⁹ Recent studies from another laboratory have shown that a number of our compounds display inhibition against glutamate-mediated excitatory synaptic transmission by modulation of GABAergic transmission.^10–11 Based on these findings, we hypothesize there is an essential pharmacophore within the enaminone structure that possibly interacts with the GABA receptor, which is significant for achieving anticonvulsant activity. Even though the exact site and structural requirements for optimal binding are unknown, we believe because of the molecular similarities between the enaminone analogues, the compounds share a common binding pocket on the GABA receptor which explains the probability of eliciting similar biological properties. Theoretically, if the chemical entities are comparable by their 3D features and found to have similar properties, which means that a high quality QSAR can be generated among those compounds, it is likely that the compounds share a common pharmacophore and and interact with the target in the same binding site as a similar mode. Therefore, 26 scPTZ active enaminones previously synthesized in our laboratory were employed to determine a pharmacophore model using the three-dimensional quantitative structure–activity relationship (3D QSAR) techniques of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); the results of these studies are presented in this paper.

Computational Method

The molecular modeling and comparative molecular field evaluations were performed using SYBYL version 7.2 running on a Silicon Graphics Tezro. The ChemDraw - Power Mac G5 1 GB memory running ChemDraw Ultra v. 8 was used to compute the CLog P values for each molecule. Default settings for these programs were used unless specified otherwise.

Data Setting

As listed in Table 1, the 26 compounds which form the training set for this study showed good activity against the chemically induced subcutaneous pentylenetetrazol (scPTZ) seizure test.⁷ The CLog P was calculated for each derivative to determine the ideal lipophilicity which represents the capability of the compounds to cross the blood brain barrier (BBB) to reach the active site.

Table 1.

Training set of diverse scPTZ active enaminone compounds.

Cyclic enaminones
Compound ID	R	R1	R2	R3	scPTZ datab
DM 50 Add a 217012	C6H4 (p-OCF3)	CH3	H	CO2CH3	2
E 144 ADD 208075	C6H4 (p-CF3)	CH3	H	CO2CH3	2
E 120 ADD 208040	C6H4 (p-F)	CH3	H	CO2CH3	2
E 139 ADD 208071	C6H4 (p-Br)	CH3	H	CO2CH3	2
LS 16 ADD 226059	C6H4 (o-F)	CH3	H	CO2CH3	2
AT 3 ADD 221002	C6H3 (m,p-Cl)	CH3	H	CO2CH3	2
E 148 ADD 210024	C6H4 (p-CF3)	CH3	CH3	CO2CH3	1
E 140 ADD 208072	C6H4 (p-Cl)	CH3	CH3	CO2CH3	2
E 196 ADD 226074	C6H4 (p-CN)	CH3	H	CO2C2H5	1
E 191 ADD 226070	C6H4 (m-OCF3)	CH3	H	CO2C2H5	2
E 183 ADD 226063	C6H3 (m, p-Cl)	CH3	H	CO2C2H5	2
E 190 ADD 226069	C6H3 (o, p-Cl)	CH3	H	CO2C2H5	2
E 167 ADD 224032	C6H3 (2′-OCH3, 5′-CH3)	CH3	H	CO2C2H5	2
DM 27 ADD 211031	C6H4 (p-Cl)	CH3	H	H	2
K-4-16 ADD 300078	C6H4 (p-OCF3)	CH3	H	H	2
K-4-33 ADD 326078	C6H4 (p-F)	CH3	H	H	2
K-4-32 ADD 326077	C6H4 (m-Cl)	CH3	H	H	2
K-4-30 ADD 326075	C6H4 (m-Br)	CH3	H	H	2
JF-1 ADD 231041	-	H	H	H	1
E 170 ADD 224035					2
MLL-5					2
Acyclic enaminones
Compound ID		X			scPTZ data b
DM 28 ADD 211005		C2H5			2
DM 31A ADD 211032		OCH3			2
DM 34 ADD 211026		Cl			2
DM 33 ADD 211025		Br			2
E 157 ADD 216059		I			2

^aADD: Antiepileptic Drug Development Program;

^bPhase 1 in mice activity – class 1 = activity at 100 mg/kg or <; class 2 = activity between 100 and 300 mg/kg

Generation of enaminone structures

CoMFA and CoMSIA studies require the coordinates of molecules to be aligned according to the reasonable bioactive conformation. So far neither the GABA receptor nor a GABA-agonist receptor complex has been solved at the structural level. In this respect, having the x-ray structure of the active anti-scPTZ compound DM 26 (class 2 activity; Figure 1), gave us a possibility to establish a reasonable rule for the structural alignment in the 3D-QSAR study. The structure of DM 26 is very similar to the group of compounds studied here except, DM 26 has a para-chloro pyridine moiety at the secondary amine whereas the other derivatives have a mono- or di-substituted phenyl ring attached to the secondary amine or amide functional group. In the 3D structure of DM26 there is an H-bond located between the nitrogen of the pyridine ring and the vinyl proton of the enaminone system, which keeps the central part of the molecule (the enaminone system (HN-C=C-CO) and the pyridine ring) flat in both the minimized and crystal structures. However, the other molecules with the exception of MLL-5 have the substituted phenyl ring instead of the pyridine ring and because they lack the H-bond, the molecules tend to twist at an angle. Hence, when the compounds were aligned in their minimized conformation, the highest q² we were able to obtain was 0.353, indicating the 3D QSAR model was not a good predictor (Figure S1 in supplementary material). Therefore, the crystal structure of DM26 was chosen as a template to establish the conformation of the compounds in the database.

Figure 1.

Alignment of the diverse scPTZ active enaminone compounds used in the training set for CoMFA and CoMSIA analysis.

The starting coordinates of the DM 26 (Scheme 1) x-ray crystal structure¹² was imported into SYBYL. Energy minimization was carried out in SYBYL using the Tripos force field and the Gasteiger-Huckel charge with distance-dependent dielectric and conjugate gradient method. The convergence criterion was 0.01 kcal/mol Å. The results showed the minimized structure of DM 26 to be similar to its crystal structure. For consistency, the energy minimized structure of DM 26 was used to construct all compounds in the data set and was later saved into the database for alignment. For compounds that shared the common cyclic enaminone backbone with DM 26, the structural modifications were performed by deletion and replacement of different side chains on the cyclic enaminone backbone via the molecular sketch option in SYBYL, followed by a systematic (conformational) search and energy minimization via calculation options in SYBYL. The latter two methods allowed the structures side chains to rotate freely. For compounds with a different backbone, DM 26 was first loaded into the molecular area and modified to the new backbone. Subsequently, the modification of the side chains, the structural systematic search, and the energy minimization of the new structures were performed as previously described.

Scheme 1.

DM 26 (ADD 207092)

Alignment

The minimized structure of each enaminone analogue was imported into SYBYL. All conformers were aligned together by least-squares fitting the five atoms of the common functional moiety of all the structures (Scheme 2). Before performing CoMFA and CoMSIA field calculations, the partial charges for each compound were calculated using the Gasteiger-Huckel method. Figure 1 depicts the alignment of structures used in the training set for CoMFA and CoMSIA analyses.

Scheme 2.

Core structure used for superimposition

CoMFA

As a 3D QSAR technique, CoMFA is based on the relationship between biological activity and the structural properties of the tested compounds. This computational technique is mainly applied when the receptor site is unknown. In this study, the aligned molecules (Figure 1) were kept in a three-dimensional grid of 2 Å in the x, y, and z directions. The steric and electrostatic properties were calculated at various grid points using an sp³ carbon probe atom with a charge of +1.0 and a van der Waals radius of 1.52 Å. The default value of 30 kcal/mol was set as a maximum steric and electrostatic energy cutoff. The regression analysis was carried out using the full cross-validated partial least-square (PLS) leave one out (LOO) method to give the q² value. The cross-validated q² indicates the predictive power of the analysis. The final model was developed with the optimal number of components determined in the regression analysis to yield the non-cross-validated PLS correlation coefficient, r², indicating the quality of data fit in the equation.

CoMSIA

In CoMSIA, five fields are calculated: steric, electrostatic, hydrophobic, hydrogen bond acceptor, and hydrogen bond donor by using the standard settings (probe with charge +1, radius 1 Å and hydrophobicity +1, hydrogen-bond donating +1, hydrogen-bond acceptor +1, attenuation factor α of 0.3 for Gaussian type distance, and grid spacing of 2 Å). This technique has several advantages over CoMFA, such as greater robustness, no application for energy cutoff, and more interpretable contour maps.^13,14 The alignment in Figure 1 was used to compute the similarity index fields for CoMSIA and the statistical analysis was carried out as previously stated for CoMFA.

Results and Discussion

Our extensive research and structure-activity relationship (SAR) studies with enaminones^8,15–20 have uncovered remarkable anticonvulsant activity for several of our analogues. The cellular mechanism studies conducted by Kombian and co-workers showed that some anticonvulsant aniline and benzylamino enaminones reversibly suppressed glutamate-mediated excitatory postsynaptic currents (EPSCs) recorded in the nucleus accumbens and hippocampus regions which is analogous to GABA activity.^10,11 The results from the study encouraged us to reinvestigate the library of scPTZ active compounds which were previously synthesized to determine a practical pharmacophore model that could be utilized to develop novel anticonvulsant enaminones by means of the 3D-QSAR techniques, CoMFA and CoMSIA.

A 3D-QSAR CoMFA study requires the superimposition of a set of active molecules that are kept in a 3D grid space as the steric and electrostatic fields are calculated at various grid points.¹³ With CoMSIA, the steric and electrostatic fields are computed as in CoMFA, but additionally the hydrophobic, hydrogen-bond donor and hydrogen-bond acceptor fields are included in the analysis. In Table 1, the training set which consists of the 26 active anti-scPTZ derivatives were developed on a structure-based design approach to identify and characterize the minimal molecular requirements for anticonvulsant activity. Previously, we reported on the correlation between CLog P and the anticonvulsant activity of our enaminone derivatives. It is known that the lipophilicity of various compounds is associated with the biological activity.¹⁸ Thus, the numerical data used in this study to develop the CoMFA and CoMSIA models were the CLog P values for each molecule.^8,21 We decided to use these data values for two reasons 1) the ED₅₀ values were not available for all of the compounds, and 2) the CLog P data provided better values for the computational analysis than the scPTZ data (denoted as class 1 or 2, shown in Table 1). In addition, the wide ranged CLogP values of these compound (−0.5 to 4.98) not only show their different capability to penetrate through the cell membrane into the suitable position of the binging site, but also offer a good sample group for the QSAR study.

By having the X-ray crystal structure of the active compound DM 26 (ADD 207092),⁷ (shown in Scheme 1) an “alignment rule”²² was established for the superimposition of the diverse set of compounds to perform both the CoMFA and CoMSIA studies (see Scheme 2 and Figure 1). 3D QSAR studies with a q² greater than 0.3 are considered to be statistically significant, although values greater than 0.4 are preferred.¹³

For the CoMFA study, a cross-validated partial least square (PLS) analysis was performed using the leave-one-out option and the SAMPLS program determined the optimal number of components to be used (five). The PLS analysis predictive power was clearly statically significant with a q² (cross-validated r²) of 0.558. Using the optimal number of components found earlier, the non-cross-validated PLS analysis resulted in an r² (non-cross-validated) of 0.895, a SEE (standard error of estimate) of 0.380, and an F value of 34.224. The relative contributions to this CoMFA model for the steric and electrostatic fields were 57.1% and 42.9%, respectively. This is an indication that the steric properties of the model have more impact on the anticonvulsant activity than the electrostatic properties.

With CoMSIA combined properties steric, electrostatic, hydrophobic, hydrogen-bond donor, and hydrogen-bond acceptor fields, gave a highly significant q² of 0.698 with an r² of 0.991. This suggests that the CoMSIA model is also a reliable predictor. Since the hydrophobicity property had greater contribution in the model (42.5%) compared to the other properties calculated, a CoMSIA analysis was performed for this field only. The hydrophobic field also showed a significant q² value of 0.688 and an r² of 0.939, indicating that lipophilicity plays a major role in the activity of the anticonvulsant enaminones. The results for both the CoMFA and CoMSIA studies are summarized in Table 2, and the calculated results are shown in Table 3.

Table 2.

Statistical Results for CoMFA and CoMSIA Models Based on 26 Active Enaminone Derivatives CLog P

	Cross-validated data					Conventional data		HDPfh	HBDfi	HBAfj
	q2a	ONCb	r2c	SEEd	Fe	Sff	Efg
CoMFA	0.558	5	0.895	0.380	34.224	0.571	0.429	-	-	-
CoMSIA	0.698	7	0.991	0.118	281.802	0.134	0.263	0.452	0.063	0.087
CoMSIA HDP only	0.688	5	0.939	0.290	61.667	-	-	-	-	-

^aq², cross-validated r²;

^bONC, optimum number of components;

^cr², non-cross validated;

^dSEE, standard error estimate;

^eF, F value;

^fSf, steric field;

^gEf, electrostatic field;

^hHDPf, hydrophobic field,

ⁱHBDf, hydrogen-bond donor field,

^jHBAf, hydrogen-bond acceptor field

Table 3.

Predicted CLog P (PCLP) vs. Calculated CLog P and Residuals (Res) by CoMFA and CoMSIA

Compound	CLog P	CoMFA		CoMSIA		CoMSIAa
		PCLP	Res	PCLP	Res	PCLP	Res
DM 50	3.14	3.55	−0.41	2.96	0.18	3.23	−0.09
E 144	4.46	4.47	−0.01	4.52	−0.06	3.95	0.51
E 120	2.25	2.34	−0.09	2.42	−0.17	2.42	−0.17
E 139	2.97	2.60	0.37	3.29	−0.32	3.36	−0.39
LS 16	2.25	2.24	0.01	2.16	0.09	2.09	0.16
AT 3	3.41	3.57	−0.16	3.50	−0.09	3.34	0.07
E 148	4.98	4.69	0.29	4.81	0.17	4.52	0.46
E 140	3.34	3.43	−0.09	3.26	0.08	3.62	−0.28
E 196	2.07	2.39	−0.32	2.24	−0.17	2.24	−0.17
E 191	3.67	3.71	−0.04	3.53	0.14	3.63	0.04
E 183	3.94	2.90	1.04	3.72	0.22	3.89	0.05
E 190	4.06	4.26	−0.20	4.25	−0.19	4.23	−0.17
E 167	3.06	3.18	−0.12	3.02	0.04	2.86	0.20
DM 27	2.96	2.99	−0.03	2.98	−0.02	2.92	0.04
K-4-16	3.27	3.04	0.23	3.49	−0.22	3.17	0.10
K-4-33	2.39	2.93	−0.54	2.40	−0.01	2.30	0.09
K-4-32	2.96	2.88	0.08	2.78	0.18	2.87	0.09
K-4-30	3.11	2.91	0.20	3.07	0.04	3.26	−0.15
JF 1	−0.35	−0.40	0.05	−0.32	−0.03	−0.23	−0.12
E 170	1.94	2.32	−0.38	1.90	0.04	2.32	−0.38
MLL-5	4.19	4.17	0.02	4.15	0.04	4.35	−0.16
DM 28	2.51	2.47	0.04	2.74	−0.23	3.01	−0.50
DM 31A	1.58	2.15	−0.57	1.48	0.10	1.08	0.50
DM 34	2.50	2.54	−0.04	2.31	0.19	2.29	0.21
DM 33	2.65	2.54	0.11	2.67	−0.02	2.73	−0.08
E 157	2.91	2.33	0.58	2.88	0.03	2.76	0.15

^aCoMSIA, only hydrophobic field

In the study, we observed when the database included DM 26, the highest q² for CoMFA only reached 0.378 indicating the model had poor predictive capability while on the other hand the CoMSIA analysis gave a q² of 0.671, suggesting the model has good predictive power. We believe the difference in the predictability for the two models is mainly due to the fact that CoMSIA considers hydrophobicity as a relative contributor whereas CoMFA does not. Therefore, to maintain consistency in this 3D-QSAR study the DM 26 was not included in the second training set, shown in Figure 1 and Table 1.

To test the predictive ability of the CoMFA and CoMSIA models, the CLog P was computed for E 121–123 and JF 37, 43, 93, and 102, which were not included in the training set. The calculated and predicted CLog P values were in good agreement for both analyses (Table 4, rFigure 2). The CoMFA model revealed a correlation coefficient 2 of 0.969 and the CoMSIA model gave an r² of 0.905. These results are a clear indication that our models have good predictive ability (Figure 2).

Table 4.

Test set data for the 3D CoMFA and CoMSIA.

Compounds ID	Structure	scPTZb	CLogP	Predicted
				CoMSIA		CoMFA
				CLogP	Residual	CLogP	Residual
JF 102 ADD a 275006		1	2.42	1.82	0.6	2.09	0.33
E 123 ADD 206043		2	2.78	2.55	0.23	2.53	0.25
E 121 ADD 206041		1	3.35	3.02	0.33	3.15	0.20
JF 37 ADD 261108		1	0.97	1.23	−0.26	0.949	0.021
JF 93 ADD 273008		1	1.97	1.84	0.13	2.06	−0.09
JF 43 ADD 261110		1	2.01	1.97	0.04	1.97	0.04

^aADD, Antiepileptic Drug Development;

^bPhase 1 in mice activity – class 1 = activity at 100 mg/kg or <; class 2 = activity between 100 and 300 mg/kg

Figure 2.

Test set observed and predicted CLog P for CoMFA (A) and CoMSIA (B) models. CoMFA (A), correlation coefficient r² = 0.9697; CoMSIA (B), correlation coefficient r² = 0.9056.

According to Golbraikh and Tropsha²³, performing both internal and external validations for 3D QSAR models are very important for obtaining reliable results. Further analytical CoMFA calculations were carried out for the validation of our model. All 32 compounds, those of the training set and the test set, were combined as a single, internally validated set. The results from the cross-validation gave a q² of 0.571, the optimal number of components (ONC) as 8, an r² of 0.977, and a SEE of 0.182. This is a clear indication that the data is highly statistically significant. For the external validation, we ran the CoMFA analyses 130 times. The 32 compounds were randomly divided into a training set (26 compounds) and a test set (6 compounds). Among those 130 calculations, 60 CoMFA calculations generated q²s higher than 0.5, and of the 60 there were four runs that gave q²s higher than 0.7, (Figure S2 in Supplementary material). However, when the model was evaluated with the test set, some of them did poorly to predict the training set. For example, the top four q² values were 0.727, 0.714, 0.709, and 0.703 respectively, and the correlation coefficients for the observed CLog P and the predicted CLog P were lower than 0.655. The CoMFA model we presented above is by far the best model which can fulfill the stringent requirements for a statically valid mathematical model set forth by Golbraikh and Tropsha.

CoMFA contour maps

The contributions of the steric and electrostatic fields for the CoMFA results were graphically displayed in contour maps with E140 acting as the reference molecule (Figure 3). The abundance of the yellow-green region to the red-blue region indicates a greater contribution of the steric field towards anticonvulsant activity than the electrostatic field. This trend is shown in the statistical data in Table 2 where the relative contribution for the steric field is 57.1% whereas the contribution of the electrostatic field is 42.9%. Based on the contour plots, the following structural optimizations are essential for enhancing the binding of diverse cyclic enaminone derivatives to the proper site on the GABA receptor to elicit anticonvulsant activity. In the steric contour plot (Panel A of Figure 4), the yellow polyhedra areas are primarily on the left end of the structure around the methyl ester moiety and the 6′-methyl, indicating that these areas of the molecule are where the addition of bulky groups might cause a decrease in activity. This is also true in the region of the para-chloro on the aromatic ring; a larger, more bulky substituent at that particular position may result in a loss of activity. The two green regions implies just the opposite of the yellow regions; bulky groups in these areas are favored and may increase anticonvulsant activity. The small green area around the 6′-methyl shows the possibility of extending the methyl group to a propyl group before moving into the yellow region. The electrostatic contour (Panel B of Figure 4) shows majority of its contribution on the opposite side of the molecule. The blue polyhedra favor electropositive and hydrogen bond donor moieties, whereas the red polyhedra are the areas where electronegative and hydrogen bond acceptor moieties are favored.

Figure 3.

Contour STDEV*COEFF map from CoMFA analysis with 2 Å grid spacing displayed with E140 as the reference molecule.

Figure 4.

Comparison of contour STDEV*COEFF maps of CoMFA and CoMSIA with E140 as reference molecule (A) Steric field distributions of CoMFA. (B) Electrostatic field distribution of CoMFA. (C) Steric field distributions of CoMSIA. (D) Electrostatic field distribution of CoMSIA. The E140 molecule is shown as the stick structure. Green contour indicate regions were bulky groups are favored to improve the activity. Yellow contour indicate regions were bulky groups are disfavored and can decreased activity. Blue contour indicate regions were electropositive substituents are favored for improving the activity. Red contour indicate regions were electronegative substitutents are favored.

CoMSIA contour maps

For the CoMSIA steric and electrostatic plots the colors represent similar meanings as in CoMFA (Figure 4). In the CoMSIA steric map (Panel C of Figure 4), the green contour about the para position of the aromatic ring indicates a region where a bulky substituent would be favorable and possibly improve activity. The yellow regions in the CoMSIA map are focused around similar areas as in the CoMFA steric map. Therefore, less bulky groups at the 6′ position on the cyclic enaminone ring and about the aromatic ring would be favorable for enhancing activity. With the electrostatic contours, the blue regions refer to areas where increased positive charged groups are favored for activity, and the red regions represent areas where electronegative substituents would be favorable for activity, are shown in Panel D of Figure 4. Panel A of Figure 5 shows the contributions of the hydrophobic field. The yellow polyhedron spanning the meta and para position of the benzene ring refer to an area where hydrophobic interactions are favorable for anticonvulsant activity, while the white region, which has a greater size than the yellow region, represent areas where hydrophilic interactions are favorable for activity. The contour maps for the H-bond donor and acceptor fields in Panel B of Figure 5, shows the cyan-color region to be favored for H-bond donor interactions, and the magenta region to be favorable for H-bond acceptor interactions. According to Figure 5, the secondary amine moiety has the possibility to act as a H-bond donor to the binding site of the receptor that may be significant for activity.

Figure 5.

Contour STDEV*COEFF maps of CoMSIA with E140 as reference molecule. (A) Contour map of the hydrophobic field distribution. (B) Contour map of hydrogen-bond donor field and hydrogen-bond acceptor field contributions. In panel A, the yellow contour is a hydrophobic favored area and the hydrophilic favored area is surrounded by the white contour. In panel B, the cyan contour favors hydrogen- bond donor interactions and the magenta contour favors the hydrogen-bond acceptor interactions.

Conclusion

This paper provides the first predictable 3D-QSAR CoMFA and CoMSIA experiments with the enaminone pharmacophore for scPTZ results. The 3D-QSAR CoMFA and CoMSIA models demonstrated a good ability to predict the CLog P of the scPTZ active enaminone analogues. The results suggest within the designed compounds there is a common pharmacophore that shares the same binding site and interacts with the target receptor in a similar mode. The outcomes from the contour maps have provided insight for the design of a novel series of anticonvulsant agents that will have greater activity by identifying significant regions for steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor interactions.